U.S. Pat. No. 4,676,215 refers to the possibility to modify values stored in a matrix memory or characteristic field and accessed in dependence on operating characteristic quantities of the internal combustion engine in accordance with a learning process, such that not only one single predetermined characteristic value is modified but also the characteristic values lying in its vicinity, with these additional modifications occurring in dependence on the modification of the characteristic value concerned. Specifically, this can be accomplished such that during the actual operation of the internal combustion engine an integral controller continuously acts in a multiplicative manner on the value read out from the characteristic field while at the same time the multiplicative correction factor of the controller is averaged. On departure from the environment of a specific support point in the matrix memory or characteristic field which is subdivided into a predetermined number of support points and wherein intermediate values are computed by linear interpolation defining the environment of each support point, the mean value is incorporated into the corresponding support point. In this manner, the characteristic field is adapted to the values predetermined by the controller by modification of the support points, so that the entire range of the anticipatory control learns adaptively. On the other hand, it is thereby avoided that only specific ranges of the characteristic field are included in the learning process which would be the case if single values were adapted. Therefore, the subject of the above-mentioned U.S. Pat. No. 4,676,215 eliminates the problem that in particular in characteristic fields with relatively fine subdivisions single values are accessed only very rarely or not at all, and consequently are not adapted. As a result, the entire characteristic field serving for the anticipatory control of corresponding operating characteristic quantities would become substantially distorted in the course of time.
In this connection, it is generally known from German published patent application DE-OS 2,847,021 and British patent application GB-PA 2,034,930B to configure mixture control systems such that the fuel is metered via so-called learning control systems. Such a learning control system stores in a characteristic field, for example, injection values for transfer to a read-write memory each time the engine is started. The characteristic fields provide for a very quick response of the precontrol of, for example, the injected fuel quantity or of fuel metering generally, or also of other quantities which are to be adapted to the changing operating conditions of an internal combustion engine as quickly as possible, including ignition point, exhaust-gas recirculation rate, and the like. In order to obtain learning control systems, the individual characteristic field values can be corrected in dependence on operating characteristic quantities and can be written into the appropriate memory.
The following explanations relate to further improvements in the control action of self-adaptive characteristic fields. At least partly and to avoid repetition, they are based on the disclosure of U.S. Pat. No. 4,676,215 the full contents of which are herewith also made the subject of the disclosure of the present application and are incorporated by reference herein.
Self-optimizing injection systems or other systems for the open and closed-loop control of operating characteristic quantities possess a characteristic field, here for the duration of injection, with rotational speed and, for example, throttle flap position as input quantities (addresses), the characteristic field being subdivided into the ranges idling, part load, full load and overrun, for example. At idling, the rotational speed is controlled, at part load the control objective is, for example, minimum fuel consumption, while it is maximum power in the full-load range. In the overrun mode of operation, the supply of fuel is cut off and, with the adaptation of the characteristic field to the individual values predetermined by the controller, a learning method for the fast control range (self-adaptive anticipatory control) is introduced. The controller referred to in the foregoing can evaluate any desirable suitable actual value quantity of the controlled system as input quantity. Its output quantity acts for the actual control area multiplicatively on the value read out from the characteristic field in dependence on the input addresses (for example, rotational speed, throttle flap position or load) and operates on the learning range of the anticipatory control (characteristic field) preferably via an averaged control factor. If the controlled system is an internal combustion engine as in this application, the engine variable evaluated as actual value may be the output signal of a Lambda sensor or some other appropriate sensor in the exhaust duct, or the engine variable may be the rotational speed of the internal combustion engine if, due to an extreme value control (wobbling) of specific controlled operating characteristic quantities (duration of injection ti, air quantity and the like), minimum fuel consumption or maximum power are the control objectives. A comprehensive description of such control methods is given in the above-mentioned U.S. Pat. No. 4,676,215.